AI predicts cholera risk from gut bacteria

May 14, 2018 //
By Rich Pell

Scientists at Duke University (Durham, NC), Massachusetts General Hospital (Boston, MA), and the International Centre for Diarrheal Disease Research (Dhaka, Bangladesh) have used machine learning to spot patterns within communities of bacteria living in the human gut that would previously have gone undetected.

Those patterns, they found, could be predictive of whether someone exposed to cholera would actually fall ill or not. But, say the researchers, it was only through the use of artificial intelligence (AI) that they were able to distinguish such subtle patterns among the trillions of bacteria comprising the human gut microbiome.

"These are patterns that even the most sophisticated scientist couldn't detect by eye," says Lawrence A. David, Ph.D., a senior author of a study on the research and assistant professor of molecular genetics and microbiology at Duke School of Medicine. "While some people are warning about artificial intelligence leading to killer robots, we are showing the positive impact of AI in its potential to overcome disease."

The research, say the scientists, suggests that a focus on gut microbes may be important for developing improved vaccines and preventive approaches for cholera and other infectious diseases.

"Our study found that this 'predictive microbiota' is as good at predicting who gets ill with cholera as the clinical risk factors that we've known about for decades," says Regina C. LaRocque, M.D., MPH, of the Massachusetts General Hospital Division of Infectious Diseases, a senior author of the study and assistant professor of medicine at Harvard Medical School. "We've essentially identified a whole new component of cholera risk that we did not know about before."

In their study, the researchers collected samples from residents of Dhaka who lived in the same household with a patient hospitalized with cholera, and who were thus at imminent risk of developing the disease. Of 76 household contacts studied, about a third went on to develop cholera during the follow-up period and about two-thirds remained uninfected.

The microbiota from the household contacts' samples were then profiled using sequencing technology and then uploaded into a computer for analysis. The researchers trained the machine to scan the results from 4000 different bacterial populations in each of the samples, looking for patterns